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Neural-network-based predictive control for nonlinear processes

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3 Author(s)
Chi-Huang Lu ; Dept. of Electr. Eng., Hsiuping Inst. of Technol., Taichung, Taiwan ; Yuan-Hai Charng ; Chi-Ming Liu

This paper presents a design methodology for generalized predictive control (GPC) using recurrent neural networks (RNNs) for a class of nonlinear processes. The discrete-time nonlinear system model using RNN is constructed with an appropriate learning rate, in order to identify the weights in the recurrent neural network model (RNNM). The proposed neural-network-based predictive controller is derived via a generalized predictive performance criterion and an appropriate learning rate for guaranteeing the convergence of the GPC controller. Two examples, including the control of a nonlinear process and the control of a physical variable-frequency oil-cooling machine, are exemplified to demonstrate the effectiveness of the proposed control approach. Both results from numerical simulations and experiments show that the proposed method is capable of controlling a class of nonlinear processes with satisfactory performance under setpoint and load changes.

Published in:

SICE Annual Conference 2010, Proceedings of

Date of Conference:

18-21 Aug. 2010